A statistical-dynamical approach to probabilistic precipitation forecasts of southern African summer rainfall is described and validated. An ensemble of seasonal precipitation and circulation fields is obtained from the ECHAM4.5 atmospheric general circulation model (AGCM). Model output statistics (MOS) then spatially recalibrate the AGCM fields relative to observations. Although the MOS equations are built using the simulation data, in which observed SSTs force the AGCM, the same set of equations can be applied to the predicted data, in which predicted SSTs force the AGCM. The use of prediction data in a set of equations developed for simulations, assumes that the AGCM forecast skill approximates its simulation skill and that the systematic biases of the AGCM do not change in a prediction setting; this assumption is analogous to a perfect prognosis (PP) approach. Probabilistic forecast skill is assessed using this MOS-PP-recalibration scheme for 3 equi-probable categories using a 3-year-out cross-validation approach. High skill scores are found over the north-eastern interior of the region, with marginal skill over the remainder of the austral summer rainfall regions. When skill is assessed for only the wettest and driest of the years, high skill appears over most of the region.